MCP Server Development

MCP Server Development

Overview

The Model Context Protocol (MCP) is an open-source standard by Anthropic for bidirectional communication between AI models and external data environments. This skill covers building production-grade MCP servers that expose tools, resources, and prompts to Claude Desktop and other MCP-compatible clients.

When to Use This Skill

• Building a new MCP server to connect Claude to an external API or data source
• Converting an existing REST API into an MCP-compatible tool server
• Designing multi-tool servers with efficient token usage (avoiding Context Bloat)
• Implementing OAuth 2.1 authentication for multi-user MCP deployments
• Optimizing server performance for enterprise-scale tool calling
• Debugging MCP transport issues (stdio, SSE, Streamable HTTP)

Core Concepts

MCP Architecture

┌──────────────────┐       Transport Layer        ┌──────────────────┐
│    MCP Client    │ ◄──────────────────────────► │    MCP Server    │
│  (Claude Desktop │   stdio | SSE | HTTP          │                  │
│   / VS Code)     │                               │  Tools           │
│                  │   JSON-RPC 2.0 Messages       │  Resources       │
│                  │ ◄──────────────────────────► │  Prompts         │
└──────────────────┘                               └──────────────────┘

Transport Mechanisms

The N × M Problem

MCP solves the integration explosion where N models × M applications = N×M custom integrations. With MCP, each app builds one server, each model builds one client, reducing total integrations to N + M.

Implementation Guide

Minimal MCP Server (TypeScript)

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "my-tool-server",
  version: "1.0.0",
});

// Define a tool
server.tool(
  "search_items",
  "Search the database for items matching a query",
  {
    query: z.string().describe("Search keyword"),
    limit: z.number().optional().default(10).describe("Max results"),
  },
  async ({ query, limit }) => {
    const results = await searchDatabase(query, limit);
    return {
      content: [{ type: "text", text: JSON.stringify(results, null, 2) }],
    };
  }
);

// Connect via stdio
const transport = new StdioServerTransport();
await server.connect(transport);

Tool Design Best Practices

1. Minimize tool count — Group related operations into fewer, smarter tools. Avoid "one tool = one API endpoint."
2. Use descriptive schemas — Zod descriptions become the model's understanding of each parameter.
3. Return structured text — Format results as readable text, not raw JSON dumps.
4. Validate inputs at the boundary — All tool inputs come from the model; sanitize them.
5. Handle errors gracefully — Return isError: true with a helpful message, never crash.

Avoiding Context Bloat

The #1 implementation problem in the MCP ecosystem. Loading too many tools consumes the model's context window with schema definitions.

Anti-pattern: Exposing 50+ granular tools (one per API endpoint)

Best practice: Use "Code Mode" — expose a high-level execution tool that lets the model write code against your API:

server.tool(
  "execute_query",
  "Execute a JavaScript function against the platform API. Available: api.listUsers(), api.getUser(id), api.createUser(data), api.updateUser(id, data)",
  { code: z.string().describe("JavaScript code to execute") },
  async ({ code }) => {
    // Sandboxed execution of model-generated code
    const result = await sandbox.run(code, { api });
    return { content: [{ type: "text", text: String(result) }] };
  }
);

OAuth 2.1 for Multi-User Servers

For remote, multi-tenant MCP servers:

import { SSEServerTransport } from "@modelcontextprotocol/sdk/server/sse.js";

// OAuth 2.1 authorization server metadata
const authConfig = {
  issuer: "https://your-server.com",
  authorization_endpoint: "/oauth/authorize",
  token_endpoint: "/oauth/token",
  registration_endpoint: "/oauth/register",
  response_types_supported: ["code"],
  grant_types_supported: ["authorization_code", "refresh_token"],
  code_challenge_methods_supported: ["S256"], // PKCE required
};

Key requirements:

• PKCE is mandatory (no client secrets for public clients)
• Support token refresh for long-running sessions
• Scope-based permissions per tool
• Per-user credential isolation

Binary File Handling

A known gap in the ecosystem. For servers that need to process PDFs, images, or binary files:

server.tool(
  "read_document",
  "Read a document from storage (supports PDF, DOCX, images)",
  { file_id: z.string() },
  async ({ file_id }) => {
    const file = await storage.getFile(file_id);

    if (file.mimeType === "application/pdf") {
      const text = await extractPdfText(file.content);
      return { content: [{ type: "text", text }] };
    }

    if (file.mimeType.startsWith("image/")) {
      const base64 = file.content.toString("base64");
      return {
        content: [{
          type: "image",
          data: base64,
          mimeType: file.mimeType,
        }],
      };
    }

    return { content: [{ type: "text", text: file.content.toString() }] };
  }
);

Testing

Manual stdio Testing

# Initialize handshake
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}' | node index.js

# With delays for multi-message sequences
{ echo '<init_msg>'; sleep 0.2; echo '<initialized_notification>'; sleep 0.2; echo '<tool_call>'; sleep 0.3; } | node index.js

Integration Testing

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";

const transport = new StdioClientTransport({ command: "node", args: ["./index.js"] });
const client = new Client({ name: "test-client", version: "1.0.0" });
await client.connect(transport);

const tools = await client.listTools();
assert(tools.tools.length === 4);

const result = await client.callTool("search_items", { query: "test" });
assert(result.content[0].type === "text");

Deployment Patterns

Security Considerations

• Never expose raw database queries or file system access without sandboxing
• Validate and sanitize all tool inputs — the model can produce unexpected values
• Use PKCE for all OAuth flows; never embed client secrets
• Implement rate limiting on remote servers
• Log all tool invocations for audit trails
• Never return sensitive credentials in tool responses

Resources

• MCP Specification
• MCP TypeScript SDK
• MCP Python SDK
• Claude Desktop Config Guide

Changelog

Part of SkillGalaxy - 10,000+ comprehensive skills for AI-assisted development.

Source: Sandeeprdy1729/skill_galaxy — distributed by TomeVault.